Stem Cell Dynamics: Exploration of the Stem Cell attractor Landscape

Stem cells have the unique ability to transform into any cell to make every type of tissue in the human body.
The growth and transformation of an embryo into a human body is an amazing self-assembling process in which stem cells are not only building blocks but also builders. The blueprint of the final edifice is inscribed in every single cell. Each cell has to read and interpret this blueprint, according to its positional information and environmental signals. As a result the cell may decide to keep its options open, self-renew and proliferate or become a mature cell and assume a specific role as a neuron, liver or heart cell for example.
In recent years no other field of science has captured our imagination more than stem cell research. The possibility to use both adult and embryonic stem cells to engineer and regenerate tissues and organs, develop therapies for degenerative conditions such as Alzheimer?s and Parkinson?s disease, multiple sclerosis, retinal degeneration, diabetes and ischemic heard disease represents probably the most significant and exciting advance in medical science that society has ever seen.
The biggest challenge in pursuit of this vision is to understand the mechanisms and factors that influence the decision of a stem cell to differentiate into a specific cell type i.e. liver cell, neuron etc.
Stem cells have been isolated from different sources and are currently grown and maintained in vitro.
However,the lack of a quantitative understanding of
the complex processes underlying cell fate determination means that it has been difficult to engineer robust and specific lineages from stem cells grown in vitro. In order to use in vitro differentiated ES cells for human stem cell therapies, it is imperative to derive mathematical models of stem cells that will aid the understanding of their behaviour and will allow the development of effective strategies to manipulate stem cell fate.
This project aims to apply mathematical modelling and analysis techniques, which are routinely used by systems engineers, to study the behaviour of embryonic stem cells in order to understand the mechanisms that control the ability of stem cells to choose between self renewal and differentiation.

Cultures of human embryonic stem cells are typically heterogeneous including both the undifferentiated stem cells and their spontaneously differentiated derivatives. It has also become apparent that the undifferentiated stem cells themselves may exist in a number of interchangeable substates comprising the stem cell compartment. The underlying hypothesis of the project is that the behaviour of human ES cell populations depends upon stochastic events relating the interconversion of undifferentiated stem cells between a number of metastable states as well as positive and negative signalling arising both from ES cells in those different substates and their differentiated derivatives. The project proposes to apply qualitative and quantitative modeling and analysis techniques, developed for nonlinear dynamical systems, to identify criteria to identify the different stem cell substates, to investigate the underlying mechanism responsible for the observed heterogeneity in undifferentiated stem cell cultures and to monitor both the stochastic interconversions of cells between those substates and their propensity to differentiate along distinct lineages.